一种改进的不完全指数复制方法

本文在考虑交易成本和投资组合动态调整的基础上,建立混合整数线性规划模型,引入内核搜索分析框架进行近似求解,并利用沪深300进行实证研究。研究发现,一是相比于基本内核搜索法,增强型内核搜索法仅在基准指数成分股数量很大时才会较大幅度提高求解质量;二是考虑投资组合动态调整的模型不仅更稳健,而且跟踪的继承性和保持性更好,尤其适用于单边市场;三是过度刻画现实交易特征一定程度上会降低不完全指数复制模型的复制和预测效果。     

Optimal plant location and air quality management under indivisibilities and economies of scale

The implications of indivisibilities and economies of scale on the optimal location of industrial plants and on the optimal choice of their pollution abatement technologies under given air quality standards are examined. Various optimization models, accounting for abatement, land development and plant relocation costs, as well as for locational economies of scale, are developed, using the integer and mixed-integer linear programming framework. A method for computing taxes and subsidies leading the system to the optimal pattern of decisions under decentralized decision-making is proposed. Finally, an application of the approach with real data of the Haifa region is implemented, illustrating the utility of the approach for environmental management.     

Classification performance of mathematical programming techniques in discriminant analysis: Results for small and medium sample sizes

The performance on small and medium-size samples of several techniques to solve the classification problem in discriminant analysis is investigated. The techniques considered are two widely used parametric statistical techniques (Fisher's linear discriminant function and Smith's quadratic function), and a class of recently proposed nonparametric estimation techniques based on mathematical programming (linear and mixed-integer programming). A simulation study is performed, analyzing the relative performance of the above techniques in the two-group case, for various small sample sizes, moderate group overlap and across six different data conditions. Training samples as well as validation samples are used to assess the classificatory performance of the techniques. The degree of group overlap and sample sizes selected for analysis in this paper are of interest in practice because they closely reflect conditions of many real data sets. The results of the experiment show that Smith's nonlinear quadratic function tends to be superior on the training samples and validation samples when the variances–covariances across groups are heterogeneous, while the mixed-integer technique performs best on the training samples when the variances–covariances are equal, and on validation samples with equal variances and discrete uniform independent variables. The mixed-integer technique and the quadratic discriminant function are also found to be more sensitive than the other techniques to the sample size, giving disproportionally inaccurate results on small samples.     

Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model

Public transport operators in rural areas have been under pressure from weak profitability and emission issues. At the same time, scattered demand for transport has been preventing logistics systems from reaching the last mile in residential areas. Multimodal transport can synergistically integrate passenger and freight demand, increase transportation network coverage, and reduce the cost of transportation, while demand-driven services improve the flexibility and reliability of operational decisions. Therefore, this paper proposes a demand-driven passenger-and-freight-integration service (DDPFIS) mode. From the perspective of routing decisions, a new mixed-integer linear programming model based on the green vehicle routing problem is formulated to assist public transport operator’s complex decision-making. In the proposed model, vehicle capacity is fully utilized through a combination of passenger and freight demand so that optimal decisions are made about vehicle routing. Numerical experiments are designed and conducted based on realistic instances with the results indicating that: the DDPFIS mode enables effective integration of different demands, leading to high-level vehicle capacity utilization and cost reduction; and compared with two conventional models of vehicle routing problems, the proposed model achieves lower fuel consumption and cost for all problem sizes. In addition, some important management insights are provided, e.g., a greater proportion of integrated service nodes is not necessarily better; and it is more suitable to provide a service for rural residents who are relatively insensitive to time.     

A model of integrated regional logistics hub in supply chain

ABSTRACT

A regional logistics hub is a constellation of multiple logistics facilities in a region of several provinces or states. This research models an integrated regional logistics hub with consideration of time-phased demand in a multi-echelon, multi-period, and capacitated logistics network. The integrated logistics hub considers three phases based on the characteristics of China’s five-year planning policy. By applying mixed integer linear programming, the greedy heuristics approach, the particle swarm optimization algorithm, and the Hungarian method, the location of logistics facilities at different echelons of a supply chain is identified and the needs for facility expansion or closing during different time periods are determined.     

The Lowry Model Revisited: Incorporating a Multizonal Input-Output Model into an Urban Land Use Allocation Model

This paper formulates a new generation of Lowry-style models that combine a multizonal input-output model and an urban land use allocation model. Three different types of models are proposed: 1) an unconstrained model, 2) a primal of a linear programming approach that includes land supply constraints, and 3) a modified dual of the linear programming approach that allocates land based on additional conditions of economic efficiency. The modified dual formulation improves upon the limitations of Lowry models and their derivatives. First, the proposed model has sound theoretical underpinnings that incorporate the production theory of input-output models and the behavioral theory of optimization models. Second, the proposed model allocates land on the basis of economic efficiency by imposing a procedure that equalizes shadow prices. And third, the proposed model is an optimization model that fully accounts for the spatial and sectoral relationships of multiplier effects determining land use demand.     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

流水型CONWIP生产系统研究

本文研究了流水型CONWIP生产系统中在制品常量、批量和加工次序的问题。以机床加工准备成本及工件库存成本和加工流动时间为优化目标．建立了CONWIP系统整数规划模型，并提出了求解问题的启发式算法。在优化机床准备及库存成本的基础上．得到各产品的生产批量和系统在制品常量。同时，提出了流水型CONWIP生产系统的排序算法（CFA），依此得到产品的较优加工次序。本研究使CONWIP模型求解得到简化且易于应用，并获得满意解。     

考虑客户服务水平的原料采购、生产计划最优化联合决策模型

现代企业愈发注重生产成本的控制和客户服务水平的提高,基于上述两点建立了一个多阶段、考虑价格折扣的采购、生产计划多目标联合决策模型,并构造相应的启发式算法,在实证中具有很高的可操作性。     

Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles

Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.     

Linear programming,busing and educational administration

Linear programming and other operations research techniques have been used to assist school administrators and planners in assigning students to schools to achieve racial balances. A linear programming approach to the school busing problem is analyzed in some detail. Six different linear programming models allow examination of the effects of changes in the parameters of the models upon assignment of students. Various ways in which school officials may use the solutions to the models and the postoptimality analysis of the models for educational planning are discussed.     

Some models of truck allocation for household refuse collection*

A number of models is developed for the selection and allocation of trucks for household refuse collection. The models vary from a simple model related to the optimal batch-size formula to a linear programming model. A minimum-cost selection is made on the basis of time per pick-up, weight per pick-up, driving time from collection area to landfill site, capacity and costs of the relevant truck-crew combinations and lengths of possible shifts. The models are equally applicable to other house-to-house collecting and distributing activities.     

Optimal policy in Markov-switching rational expectations models

In this paper we consider the optimal quadratic control problem of Markov-switching linear rational expectation models. These models are general and flexible tools for modelling not only regime but also model or parameter uncertainty. We show, first, how to find the solution of a Markov-switching linear rational expectation model. Based on this solution we then show how to apply dynamic programming to find the optimal time-consistent policy and the resulting Nash-Stackelberg equilibrium. Suitable modifications of the algorithm allow to deal with the (non-RE) case in which the policymaker and the private sector hold different beliefs or probabilities over regime change. We also show how the optimisation procedure can be employed to obtain the optimal policy under commitment. As an illustration we compute the optimal policy in a small open economy subject to stochastic structural breaks in some of its key parameters.     

Two heuristics for the deterministic,single operator,multiple machine,multiple run cyclic scheduling problem

The semiautomatic nature of machinery often makes it economical to assign more than one machine to a single operator. Multi-machine assignments are common in the textile, tooling and molding industries. They are also found where numerically controlled (NC) machinery are used and, more recently, where flexible manufacturing systems (FMS) are employed. Previous work on deterministic cyclic scheduling models has focused on determining the optimal number of usually identical machines with single runs to assign to an operator. In practice, the schedules are represented by man/machine charts.When, due to production requirements, we mix different types of runs with known times on non-identical machines, we have the deterministic, single operator, multiple machine, multiple run, cyclic scheduling problem. We present two heuristics for solving this more realistic generalization of earlier problems. For the nonidentical, multiple run case, the scheduling of the runs is crucial in minimizing the cycle time of the system. The integer programming formulation of small problems is large, and solving it directly could require excessive computation time on a large mainframe computer.Heuristic 1 selects the next machine run to schedule by minimizing the total immediate waiting cost of the operator and machines. The hourly machine costs reflect the relative merit of utilizing certain machines over others. Heuristic 2 first schedules the machine with the longest automatic processing time of run one. It then follows Heuristic 1 until the long processing time of the first run has ended. The next available run with the longest automatic processing time is then scheduled, and the process repeats. The underlying notion is that many short runs may be performed during the long automatic run of a machine.The heuristics are polynomially bounded, can be easily implemented on a mini- or micro-computer and in practice should be much faster than integer programming methods. In addition to the heuristics, we compute a lower bound on the cycle time. We use this bound as a measure of the effectiveness of a solution. If for a given schedule, the cycle time equals its lower bound, then the solution is optimal.Both heuristics were coded in FORTRAN on a CDC-6600 computer. An interactive version was also developed for a DEC PDP11/70. A detailed computational study is presented. In it, both heuristics solved 50 machine, 5 run problems in less than 10 CPU seconds on the CDC-6600. Computational experience indicates that the heuristics are efficient and often find schedules which have cycle times within 10% the lower bound.     

区间规划在证券投资组合问题中的运用

本文基于区间规划的方法研究了摩擦市场的投资组合选择问题。文中把风险证券的收益率、投资风险及证券的流动性用区间数来描述,并结合绝对偏差风险函数的思想建立了一种关于区间数的证券投资组合选择模型。最后利用区间数的两种序关系将所提出的模糊线性规划问题转化为普通的参数线性规划问题进而求其解。     

石油产品供应链二级分销网络的优化

结合我国石油销售企业的分销网络运营方式,以单炼厂、多油库、多加油站、多产品的石油产品分销网络为研究对象,在需求不确定的条件下,建立了以总成本最小为目标的混合整数规划模型,并进一步讨论如何将模型中的机会约束转化为确定性等价类问题。     

A multiobjective modeling approach to locating incinerators

The location of hazardous material incineration facilities is an important problem due to the environmental, social, and economic impacts that they impose. The costs associated with the facilities and the risks placed on nearby populations are important concerns as are the distributions of these costs and risks. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates a Gaussian dispersion model and a multiobjective optimization model in a GIS-based interactive decision support system that planners can access via the Internet. The proposed approach is demonstrated via a case study in central Portugal where the national government has decided to locate a large facility for the incineration of hazardous industrial waste. Due to intense local and national opposition, construction of the facility has been delayed. The system has been designed so that it can be used by decision makers with no special training in dispersion modeling, multiobjective programming, or GIS.     

A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations

In principle, hours-of-service (HOS) regulations are intended to help ensure truck drivers get adequate rest and perform safe operations. The new HOS regulations, however, may lead to substantial cost increases for regional common carriers which have already been hit hard by rising fuel prices and declining shipping demands. In addition, the new HOS regulations complicate driver schedules by not only restricting the driver's consecutive driving hours, but also expanding off-duty hours. To deal with this complex challenge, we propose a model-based decision support system (DSS) that helps determine the truck driver's working hours, rest periods, and his/her assigned truck's schedules and routes under HOS regulations. As a core of this model-based DSS, we developed a mixed-integer programming model and a simulated annealing metaheuristic for solving it. This model was also integrated with a geographical information system and relational database management system to enhance interfaces between the model and its parametric data using spatial, graphical displays.     

基于JIT看板管理的混合型供应链系统库存研究

文章分析了现实中供应链系统结构的复杂性,考虑了供应链系统中生产费用、运输费用、库存费用和订货费用之间相互影响的因素,以降低整个供应链系统总费用为目标,提出了基于JIT看板管理的混合型供应链系统库存问题的数学模型,该数学模型属于混合整数非线性规划问题,混合整数非线性规划问题求解时间与整数变量数量的关系是F=∑n!,文章设计了基于分解技术的算法,通过实例证明了该算法对于大规模混合整数非线性规划问题的有效性和适用性。     

STOCHASTIC INTEGER PROGRAMMING BY DYNAMIC PROGRAMMING

Stochastic integer programming is a suitable tool for modeling hierarchical decision situations with combinatorial features. In continuation of our work on the design and analysis of heuristics for such problems, we now try to find optimal solutions. Dynamic programming techniques can be used to exploit the structure of two–stage scheduling, bin packing and multiknapsack problems. Numerical results for small instances of these problems are presented.